Study and Implementation of Clustering Algorithms in R

clustering is a process in which we group the data by finding similarities between data based on their characteristics. These groups are called cluster. In clustering, there is a division of data into groups of similar objects. These groups are the clusters, consists of objects that are similar between themselves and dissimilar compared to objects of other groups. Clustering is unsupervised learning technique, based on the concept of maximize intra-clustering and minimize inter- clustering. Nowadays, clustering of biological dataset is the widely researched topic among computer science. Bio- informatics has become area that receive most of the attention of data mining techniques. Generally, bio- informatics targets to solve complicated problems like gene categorization and its functionality, gene expression analysis of data obtained from micro- array experiments etc. These clustering techniques are addressed with R. Clustering techniques are used to analyze the structure of biological data. There are many different methods but we study k- means, Hierarchical and Density- based clustering algorithm for Biological Data using R programming tool.